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• FOUNDATIONS COMPUTATIONAL INTELLIGENCE EVOLUTIONARY COMPUTATION CONCEPTS AND PARADIGMS EVOLUTIONARY COMPUTATION IMPLEMENTATIONS NEURAL NETWORK CONCEPTS AND PARADIGMS NEURAL NETWORK IMPLEMENTATIONS FUZZY SYSTEMS CONCEPTS AND PARADIGMS FUZZY SYSTEMS IMPLEMENTATIONS COMPUTATIONAL INTELLIGENCE IMPLEMENTATIONS PERFORMANCE METRICS ANALYSIS AND EXPLANATION CASE STUDY SUMMARIES.
• (source: Nielsen Book Data)

Computational Intelligence: Concepts to Implementations provides the most complete and practical coverage of computational intelligence tools and techniques to date. This book integrates various natural and engineering disciplines to establish Computational Intelligence. This is the first comprehensive textbook on the subject, supported with lots of practical examples. It asserts that computational intelligence rests on a foundation of evolutionary computation. This refreshing view has set the book apart from other books on computational intelligence. This book lays emphasis on practical applications and computational tools, which are very useful and important for further development of the computational intelligence field. Focusing on evolutionary computation, neural networks, and fuzzy logic, the authors have constructed an approach to thinking about and working with computational intelligence that has, in their extensive experience, proved highly effective. The book moves clearly and efficiently from concepts and paradigms to algorithms and implementation techniques by focusing, in the early chapters, on the specific con. It explores a number of key themes, including self-organization, complex adaptive systems, and emergent computation. It details the metrics and analytical tools needed to assess the performance of computational intelligence tools. The book concludes with a series of case studies that illustrate a wide range of successful applications. This book will appeal to professional and academic researchers in computational intelligence applications, tool development, and systems.
(source: Nielsen Book Data)

• Series Foreword; Preface; 1
• Introduction to Semi-Supervised Learning; 2
• A Taxonomy for Semi-Supervised Learning Methods; 3
• Semi-Supervised Text Classification Using EM; 4
• Risks of Semi-Supervised Learning: How Unlabeled Data Can Degrade Performance of Generative Classifiers; 5
• Probabilistic Semi-Supervised Clustering with Constraints; 6
• Transductive Support Vector Machines; 7
• Semi-Supervised Learning Using Semi- Definite Programming; 8
• Gaussian Processes and the Null-Category Noise Model; 9
• Entropy Regularization; 10
• Data-Dependent Regularization.

• 11
• Label Propagation and Quadratic Criterion12
• The Geometric Basis of Semi-Supervised Learning; 13
• Discrete Regularization; 14
• Semi-Supervised Learning with Conditional Harmonic Mixing; 15
• Graph Kernels by Spectral Transforms; 16- Spectral Methods for Dimensionality Reduction; 17
• Modifying Distances; 18
• Large-Scale Algorithms; 19
• Semi-Supervised Protein Classification Using Cluster Kernels; 20
• Prediction of Protein Function from Networks; 21
• Analysis of Benchmarks; 22
• An Augmented PAC Model for Semi- Supervised Learning.

• 23
• Metric-Based Approaches for Semi- Supervised Regression and Classification24
• Transductive Inference and Semi-Supervised Learning; 25
• A Discussion of Semi-Supervised Learning and Transduction; References; Notation and Symbols; Contributors; Index.

In the field of machine learning, semi-supervised learning (SSL) occupies the middle ground, between supervised learning (in which all training examples are labeled) and unsupervised learning (in which no label data are given). Interest in SSL has increased in recent years, particularly because of application domains in which unlabeled data are plentiful, such as images, text, and bioinformatics. This first comprehensive overview of SSL presents state-of-the-art algorithms, a taxonomy of the field, selected applications, benchmark experiments, and perspectives on ongoing and future research.Semi-Supervised Learning first presents the key assumptions and ideas underlying the field: smoothness, cluster or low-density separation, manifold structure, and transduction. The core of the book is the presentation of SSL methods, organized according to algorithmic strategies. After an examination of generative models, the book describes algorithms that implement the low-density separation assumption, graph-based methods, and algorithms that perform two-step learning. The book then discusses SSL applications and offers guidelines for SSL practitioners by analyzing the results of extensive benchmark experiments. Finally, the book looks at interesting directions for SSL research. The book closes with a discussion of the relationship between semi-supervised learning and transduction.Olivier Chapelle and Alexander Zien are Research Scientists and Bernhard Scholkopf is Professor and Director at the Max Planck Institute for Biological Cybernetics in Tubingen. Scholkopf is coauthor of Learning with Kernels (MIT Press, 2002) and is a coeditor of Advances in Kernel Methods: Support Vector Learning (1998), Advances in Large-Margin Classifiers (2000), and Kernel Methods in Computational Biology (2004), all published by The MIT Press.
(source: Nielsen Book Data)
A comprehensive review of an area of machine learning that deals with the use of unlabeled data in classification problems: state-of-the-art algorithms, a taxonomy of the field, applications, benchmark experiments, and directions for future research. In the field of machine learning, semi-supervised learning (SSL) occupies the middle ground, between supervised learning (in which all training examples are labeled) and unsupervised learning (in which no label data are given). Interest in SSL has increased in recent years, particularly because of application domains in which unlabeled data are plentiful, such as images, text, and bioinformatics. This first comprehensive overview of SSL presents state-of-the-art algorithms, a taxonomy of the field, selected applications, benchmark experiments, and perspectives on ongoing and future research.Semi-Supervised Learning first presents the key assumptions and ideas underlying the field: smoothness, cluster or low-density separation, manifold structure, and transduction. The core of the book is the presentation of SSL methods, organized according to algorithmic strategies. After an examination of generative models, the book describes algorithms that implement the low-density separation assumption, graph-based methods, and algorithms that perform two-step learning. The book then discusses SSL applications and offers guidelines for SSL practitioners by analyzing the results of extensive benchmark experiments. Finally, the book looks at interesting directions for SSL research. The book closes with a discussion of the relationship between semi-supervised learning and transduction.
(source: Nielsen Book Data)

The book provides an overview of recent developments in large margin classifiers, examines connections with other methods (e.g., Bayesian inference), and identifies strengths and weaknesses of the method, as well as directions for future research. The concept of large margins is a unifying principle for the analysis of many different approaches to the classification of data from examples, including boosting, mathematical programming, neural networks, and support vector machines. The fact that it is the margin, or confidence level, of a classification-that is, a scale parameter-rather than a raw training error that matters has become a key tool for dealing with classifiers. This book shows how this idea applies to both the theoretical analysis and the design of algorithms. The book provides an overview of recent developments in large margin classifiers, examines connections with other methods (e.g., Bayesian inference), and identifies strengths and weaknesses of the method, as well as directions for future research. Among the contributors are Manfred Opper, Vladimir Vapnik, and Grace Wahba.
(source: Nielsen Book Data)

The book provides an overview of recent developments in large margin classifiers, examines connections with other methods (e.g., Bayesian inference), and identifies strengths and weaknesses of the method, as well as directions for future research. The concept of large margins is a unifying principle for the analysis of many different approaches to the classification of data from examples, including boosting, mathematical programming, neural networks, and support vector machines. The fact that it is the margin, or confidence level, of a classification-that is, a scale parameter-rather than a raw training error that matters has become a key tool for dealing with classifiers. This book shows how this idea applies to both the theoretical analysis and the design of algorithms. The book provides an overview of recent developments in large margin classifiers, examines connections with other methods (e.g., Bayesian inference), and identifies strengths and weaknesses of the method, as well as directions for future research. Among the contributors are Manfred Opper, Vladimir Vapnik, and Grace Wahba.
(source: Nielsen Book Data)

• Rethinking the Human-Agent Relationship: Which Social Cues Do Interactive Agents Really Need to Have? / Astrid Weiss, Manfred Tscheligi
• Believability Through Psychosocial Behaviour: Creating Bots That Are More Engaging and Entertaining / Christine Bailey, Jiaming You, Gavan Acton, Adam Rankin
• Actor Bots / Maria Arinbjarnar, Daniel Kudenko
• Embodied Conversational Agent Avatars in Virtual Worlds: Making Today's Immersive Environments More Responsive to Participants / Jacquelyn Ford Morie, Eric Chance, Kip Haynes, Dinesh Rajpurohit
• Human-Like Combat Behaviour via Multiobjective Neuroevolution / Jacob Schrum, Igor V. Karpov, Risto Miikkulainen
• Believable Bot Navigation via Playback of Human Traces / Igor V. Karpov, Jacob Schrum, Risto Miikkulainen
• A Machine Consciousness Approach to the Design of Human-Like Bots / Raúl Arrabales, Jorge Muñoz, Agapito Ledezma, German Gutierrez
• ConsScale FPS: Cognitive Integration for Improved Believability in Computer Game Bots / Raúl Arrabales, Agapito Ledezma, Araceli Sanchis
• Assessing Believability / Julian Togelius, Georgios N. Yannakakis, Sergey Karakovskiy, Noor Shaker
• Creating a Personality System for RTS Bots / Jacek Mańdziuk, Przemysław Szałaj
• Making Diplomacy Bots Individual / Markus Kemmerling, Niels Ackermann, Mike Preuss
• Towards Imitation of Human Driving Style in Car Racing Games / Jorge Muñoz, German Gutierrez, Araceli Sanchis.

We share our modern world with bots - chatbots to converse with, roombots to clean our houses, spambots to fill our e-mail inboxes, and medibots to assist our surgeons. This book is about computer game bots, virtual companions who accompany us in virtual worlds or sharpen our fighting skills. These bots must be believable, that is human players should believe they are interacting with entities operating at a human level - bots are more fun if they behave like we do. This book shows how to create believable bots that play computer games, and it discusses the implications of making them appear human. The chapters in this book present the state of the art in research on and development of game bots, and they also look beyond the design aspects to address deep questions: Is a bot that plays like a person intelligent? Does it have emotions? Is it conscious? The topic is inherently interdisciplinary, and the work draws from research and practice in many fields, such as design, creativity, entertainment, and graphics; learning, psychology, and sociology; artificial intelligence, embodiment, agents, machine learning, robotics, human-computer interaction, and artificial life; cognition and neuroscience; and evolutionary computing. The contributing authors are among the leading researchers and developers in this field, and most of the examples and case studies involve analysis of commercial products. The book will be of value to graduate students and academic researchers in artificial intelligence, and to engineers charged with the design of entertaining games.
(source: Nielsen Book Data)

• Organic Computing
• A Paradigm Shift for Complex Systems; Preface; Contents; Review Team; Projects; Contributors; Chapter 1: Theoretical Foundations; Chapter 1.1: Adaptivity and Self-organisation in Organic Computing Systems; Chapter 1.2: Quantitative Emergence; Chapter 1.3: Divergence Measures as a Generalised Approach to Quantitative Emergence; Chapter 1.4: Emergent Control; Chapter 1.5: Constraining Self-organisation Through Corridors of Correct Behaviour: The Restore Invariant Approach; Chapter 1.6: Ant Inspired Methods for Organic Computing.

• Chapter 1.7: Organic Computing: Metaphor or Model?
• Chapter 2: Methods and Tools; Chapter 2.1: Model-Driven Development of Self-organising Control Applications; Chapter 2.2: How to Design and Implement Self-organising Resource-Flow Systems; Chapter 2.3: Monitoring and Self-awareness for Heterogeneous, Adaptive Computing Systems; Chapter 2.4: Generic Emergent Computing in Chip Architectures; Chapter 2.5: Multi-objective Intrinsic Evolution of Embedded Systems; Chapter 2.6: Organisation-Oriented Chemical Programming; Chapter 2.7: Hovering Data Clouds for Organic Computing;
• Chapter 3: Learning.

• Chapter 3.1: Aspects of Learning in OC SystemsChapter 3.2: Combining Software and Hardware LCS for Lightweight On-chip Learning; Chapter 3.3: Collaborative Learning by Knowledge Exchange; Chapter 3.4: A Framework for Controlled Self-optimisation in Modular System Architectures; Chapter 3.5: Increasing Learning Speed by Imitation in Multi-robot Societies; Chapter 3.6: Learning to Look at Humans;
• Chapter 4: Architectures; Chapter 4.1: Observation and Control of Organic Systems; Chapter 4.2: Organic Computing Middleware for Ubiquitous Environments.

• Chapter 4.3: DodOrg-A Self-adaptive Organic Many-core ArchitectureChapter 4.4: The Artificial Hormone System-An Organic Middleware for Self-organising Real-Time Task Allocation; Chapter 4.5: ORCA: An Organic Robot Control Architecture; Chapter 4.6: The EPOC Architecture-Enabling Evolution Under Hard Constraints; Chapter 4.7: Autonomic System on Chip Platform;
• Chapter 5: Applications; Chapter 5.1: Organic Traffic Control; Chapter 5.2: Methods for Improving the Flow of Traffic; Chapter 5.3: Applying ASoC to Multi-core Applications for Workload Management.

• Chapter 5
• .4: Efficient Adaptive Communication from Resource-Restricted TransmittersChapter 5
• .5: OrganicBus: Organic Self-organising Bus-Based Communication Systems; Chapter 5
• .6: OC Principles in Wireless Sensor Networks; Chapter 5
• .7: Application of the Organic Robot Control Architecture ORCA to the Six-Legged Walking Robot OSCAR; Chapter 5
• .8: Energy-Awareness in Self-organising Robotic Exploration Teams; Chapter 5
• .9: A Fast Hierarchical Learning Approach for Autonomous Robots; Chapter 5
• .10: Emergent Computing with Marching Pixels for Real-Time Smart Camera Applications.

Organic Computing has emerged as a challenging vision for future information processing systems. Its basis is the insight that we will increasingly be surrounded by and depend on large collections of autonomous systems, which are equipped with sensors and actuators, aware of their environment, communicating freely, and organising themselves in order to perform actions and services required by the users. These networks of intelligent systems surrounding us open fascinating application areas and at the same time bear the problem of their controllability. Hence, we have to construct such systems as robust, safe, flexible, and trustworthy as possible. In particular, a strong orientation towards human needs as opposed to a pure implementation of the technologically possible seems absolutely central. The technical systems, which can achieve these goals will have to exhibit life-like or "organic" properties. "Organic Computing Systems" adapt dynamically to their current environmental conditions. In order to cope with unexpected or undesired events they are self-organising, self-configuring, self-optimising, self-healing, self-protecting, self-explaining, and context-aware, while offering complementary interfaces for higher-level directives with respect to the desired behaviour. First steps towards adaptive and self-organising computer systems are being undertaken. Adaptivity, reconfigurability, emergence of new properties, and self-organisation are hot topics in a variety of research groups worldwide. This book summarises the results of a 6-year priority research program (SPP) of the German Research Foundation (DFG) addressing these fundamental challenges in the design of Organic Computing systems. It presents and discusses the theoretical foundations of Organic Computing, basic methods and tools, learning techniques used in this context, architectural patterns and many applications. The final outlook shows that in the mean-time Organic Computing ideas have spawned a variety of promising new projects

• Neural Networks.- Complex Functional Network Hebbian-Type Learning Algorithm and Convergence.- A New Intelligent Control Strategy of High-Voltage Power Supply for ECRH Based on CMAC Neural Network.- Self-configuration Using Artificial Neural Networks.- Evolutionary Learning and Genetic Algorithms.- An Improvement of AdaBoost for Face Detection with Random Forests.- Hybrid Good Point Set Evolutionary Strategy for Constrained Optimization.- Research of Modified Quantum Genetic Algorithm and It's Application in Collision Detection.- Granular Computing and Rough Sets.- A New Method of Attribute Reduction and Prediction in Fuzzy Decision System.- Particle Swarm Optimization and Niche Technology.- Development of Automatic Code Generation Tool for Condensation Algorithm.- Swarm Intelligence and Optimization.- A New Hybrid Multi-objective Pareto Archive PSO Algorithm for a Classic Job Shop Scheduling Problem with Ready Times.- Multi-objective Particle Swarm Optimization for Sequencing and Scheduling a Cellular Manufacturing System.- A Hybrid PSO Algorithm with Transposon for Multiobjective Optimization.- Independent Component Analysis and Blind Source Separation.- Joint Multichannel Blind Speech Separation and Dereverberation: A Real-Time Algorithmic Implementation.- An Efficient Pairwise Kurtosis Optimization Algorithm for Independent Component Analysis.- Combinatorial and Numerical Optimization.- The Mechanical Behavior of the Double Piece of Tape Spring.- Systems Biology and Computational Biology.- Cancer Immunoprevention: What Can We Learn from in Silico Models?.- Oscillatory Dynamics of Double Negative Feedback Loop Motif by MicroRNAs.- Neural Computing and Optimization.- Neural Network Approach for Greenery Warranty Systems.- Knowledge Discovery and Data Mining.- Comprehensive Evaluation of Effects of Naomaitong and Rhubarb Aglycone Combined with Bone Mesenchymal Stem Cells Transplantation on Brain in Rats with Cerebral Ischemia Based on Analytic Hierarchy Process.- Aggregating and Weighting Expert Knowledge in Group Decision Making.- A New Heuristic Feature Selection Algorithm Based on Rough Sets.- Developing a Procedure to Obtain Knowledge of Optimum Solutions in a Travelling Salesman Problem.- Trajectory Simplification and Classification for Moving Object with Road-Constraint.- Training a Pac-Man Player with Minimum Domain Knowledge and Basic Rationality.- Hybrid Self-Organizing Map and Neural Network Clustering Analysis for Technology Professionals Turnover Rate Forecasting.- Ensemble Methods.- A Study of Strength and Correlation in Random Forests.- Machine Learning Theory and Methods.- A Comparison Study of Conditional Random Fields Toolkits.- Robot Reinforcement Learning Based on Learning Classifier System.- Intelligent Computing in Bioinformatics.- Research of Marker Gene Selection for Tumor Classfication Based on Decision Forests.- Reverse Engineered Gene Networks Reveal Markers Predicting the Outcome of Breast Cancer.- New Tools for Expression Alternative Splicing Validation.- Intelligent Computing in Computational Biology and Drug Design.- Application for Artificial Bee Colony Algorithm in Migration of Mobile Agent.- Computational Genomics and Proteomics.- A Novel Tool for Assisted In-silico Cloning and Sequence Editing in Molecular Biology.- Intelligent Computing in Signal Processing.- A New Method Using Table to Sparse Representation in Pairs of Bases with Matching Pursuits.- Intelligent Computing in Pattern Recognition.- Palmprint Recognition Method Using WTA-ICA Based on 2DPCA.- Study of TCM Diagnosis of Syndromes of Acute Exacerbation of Chronic Obstructive Pulmonary Disease Based on Dynamic Fuzzy Kohonen Network.- Classification and Characteristics of TCM Syndromes of Chronic Respiratory Failure Based on Self-adaptive Fuzzy Inference System.- Implementation of the Pattern Matching System to Detect Flip Chip PCB Defects.- JPEG Steganalysis Using Estimated Image and Markov Model.- Applications of Two-Dimensional Heteroscedastic Discriminant Analysis in Face Recognition.- Intelligent Computing in Image Processing.- Multimodal Image Fusion Algorithm Using Dual-Tree Complex Wavelet Transform and Particle Swarm Optimization.- An Adaptive Method for Lane Marking Detection Based on HSI Color Model.- An Image Data Hiding Method Using Pixel-Based JND Model.- Intelligent Computing Applications Based on Eye Gaze: Their Role in Medical Image Interpretation.- Intelligent Computing in Communication and Computer Networks.- A Sequential Test Based Cooperative Spectrum Sensing Scheme Using Fuzzy Logic for Cognitive Radio Networks.- Solving Base Station Subsystem Assignment Problem in Mobile Communication Networks Using Hybridized Heuristic Algorithm.- A Sequential Cooperative Spectrum Sensing Scheme Based on Dempster Shafer Theory of Evidence.- High Resolution Direction Finding of Multiple Coherent Signals.- Extract and Maintain the Most Helpful Wavelet Coefficients for Continuous K-Nearest Neighbor Queries in Stream Processing.- A Neural Network-Based Cooperative Spectrum Sensing Scheme for Cognitive Radio Systems.- Intelligent Computing in Robotics.- Comparison of the Observability Indices for Robot Calibration considering Joint Stiffness Parameters.- Intelligent Computing in Computer Vision.- Human Computer Interaction Using Hand Gestures.- Smart Wheelchair Navigation Based on User's Gaze on Destination.- Entrance Detection of Building Component Based on Multiple Cues.- Intelligent Prediction and Time Series Analysis.- Developing an Evolutionary Neural Network Model for Stock Index Forecasting.- A Tower-Shadow Model for Wind Turbines Using a Wavelet-Prony Method.- Special Session on New Hand-Based Biometric Methods.- Contrast Enhancement and Metrics for Biometric Vein Pattern Recognition.- Feature Extraction Method for Contactless Palmprint Biometrics.- Special Session on Theories and Applications in Advanced Intelligent Computing.- Numerical Analysis for Stochastic Investment System with Poisson Jumps.- Quantum Collapsing Median Filter.- An Impulse C Application in the LDPC Decoding Algorithm.- Research on Algorithm of Parallel Garbage Collection Based on LISP 2 for Multi-core System.- Study on Anode Effect Prediction of Aluminium Reduction Applying Wavelet Packet Transform.- Rotating Machinery Fault Diagnosis Based on EMD-Approximate Entropy and LS-SVM.- Study of Applications Based on Measurement Technology in the Future Smart Grid.- Palm Line Extraction Using FRIT.- Fast ICA for Multi-speaker Recognition System.- Variable Universe Adaptive Fuzzy-PI Compound Control Applied in Maximum Power Point Tracking for Photovoltaic Energy Generation System.- Integrated and Automated Dielectric Measurement System at Millimeter Wavelengths.- Special Session on Search Based Software Engineering.- Software Security Testing of Web Applications Based on SSD.- Special Session on Bio-inspired Computing and Applications.- A Review of Bacterial Foraging Optimization Part I: Background and Development.- A Review of Bacterial Foraging Optimization Part II : Applications and Challenges.- Liquidity Risk Portfolio Optimization Using Swarm Intelligence.- Special Session on Advance in Dimensionality Reduction Methods and Its Applications.- Dimension Reduction with Semi-supervised Pairwise Covariance-Preserving Projection.- Special Session on Recent Advances in Medical Informatics.- Analysis of Impact Factors in Acupuncture for Patients with Migraine--Doubts on Prof. Andrew J Vickers' Conclusion.
• (source: Nielsen Book Data)

• Neural Networks.- Design of a Novel Six-Dimensional Force/Torque Sensor and Its Calibration Based on NN.- Incremental-Based Extreme Learning Machine Algorithms for Time-Variant Neural Networks.- Global Exponential Robust Stability of Hopfield Neural Networks with Reaction-Diffusion Terms.- Direct Inverse Model Control Based on a New Improved CMAC Neural Network.- Further Research on Extended Alternating Projection Neural Network.- Global Synchronization in an Array of Hybrid Coupling Neural Networks with Multiple Time-Delay Components.- Colour Image Segmentation Based on a Spiking Neural Network Model Inspired by the Visual System.- The White Noise Impact on the Optimal Performance of the Hopfield Neural Network.- The Study and Realization of Virtual Organization File System Based on DHT Technology.- Evolutionary Learning and Genetic Algorithms.- A Novel Quantum Genetic Algorithm for PID Controller.- Research on Hybrid Evolutionary Algorithms with Differential Evolution and GUO Tao Algorithm Based on Orthogonal Design.- An Improved Evolution Strategy for Constrained Circle Packing Problem.- Lecture Notes in Computer Science: Research on Multi-robot Avoidance Collision Planning Based on XCS.- Fuzzy Theory and Models.- An Integrated Method for the Construction of Compact Fuzzy Neural Models.- Scalarization of Type-1 Fuzzy Markov Chains.- Fuzzy Systems and Soft Computing.- Applying Fuzzy Differential Equations to the Performance Analysis of Service Composition.- Lattice Structures of Fuzzy Soft Sets.- A Predicate Formal System of Universal Logic with Projection Operator.- A Neuro-Evolutive Interval Type-2 TSK Fuzzy System for Volatile Weather Forecasting.- A Soft Computing Approach for Obtaining Transition Regions in Satellite Images.- Particle Swarm Optimization and Niche Technology.- Particle Swarm Optimization for Two-Stage Fuzzy Generalized Assignment Problem.- A Novel Cyclic Discrete Optimization Framework for Particle Swarm Optimization.- Economic Dispatch Considering Ancillary Service Based on Revised Particle Swarm Optimization Algorithm.- Particle Swarm Optimization-Based Extremum Seeking Control.- Image Contour Extraction Based on Ant Colony Algorithm and B-snake.- Supervised and Semi-supervised Learning.- An Improved Hidden Markov Model for Literature Metadata Extraction.- Discriminative Training of Subspace Gaussian Mixture Model for Pattern Classification.- Unsupervised and Reinforcement Learning.- A Stage by Stage Pruning Algorithm for Detecting the Number of Clusters in a Dataset.- Adaptive Independent Component Analysis by Modified Kernel Density Estimation.- Combinatorial and Numerical Optimization.- Cross System Bank Branch Evaluation Using Clustering and Data Envelopment Analysis.- He's Variational Iteration Method for Solving Convection Diffusion Equations.- GRASP for Low Autocorrelated Binary Sequences.- miRNA Target Prediction Method Based on the Combination of Multiple Algorithms.- Imperialistic Competitive Algorithm for Solving a Dynamic Cell Formation Problem with Production Planning.- Systems Biology and Computational Biology.- Genome-Wide DNA Methylation Profiling in 40 Breast Cancer Cell Lines.- GRIDUISS - A Grid Based Universal Immune System Simulator Framework.- Performance Comparison of Tumor Classification Based on Linear and Non-linear Dimensionality Reduction Methods.- Neural Computing and Optimization.- PH Optimal Control in the Clarifying Process of Sugar Cane Juice Based on DHP.- Nature Inspired Computing and Optimization.- Parameter-Free Deterministic Global Search with Simplified Central Force Optimization.- Comparison of Optimality and Robustness between SA, TS and GRASP Metaheuristics in FJSP Problem.- Hardware Emulation of Bacterial Quorum Sensing.- Knowledge Discovery and Data Mining.- Finding Research Community in Collaboration Network with Expertise Profiling.- The Ideal Data Representation for Feature Extraction of Traditional Malay Musical Instrument Sounds Classification.- Mining Reputation of Person/Product from Comment and Reply on UCC/Internet Article.- Interaction Analysis for Adaptive User Interfaces.- Unsupervised Subjectivity-Lexicon Generation Based on Vector Space Model for Multi-Dimensional Opinion Analysis in Blogosphere.- Enhancing Negation-Aware Sentiment Classification on Product Reviews via Multi-Unigram Feature Generation.- Building Associated Semantic Overlay for Discovering Associated Services.- Artificial Life and Artificial Immune Systems.- Immunity-Based Model for Malicious Code Detection.- Sparse Representation-Based Face Recognition for One Training Image per Person.- Semi-supervised Local Discriminant Embedding.- Orthogonal Discriminant Local Tangent Space Alignment.- Intelligent Computing in Image Processing.- Separating Pigment Components of Leaf Color Image Using FastICA.- Fast Algorithm for Multisource Image Registration Based on Geometric Feature of Corners.- Newborn Footprint Recognition Using Subspace Learning Methods.- Plant Classification Using Leaf Image Based on 2D Linear Discriminant Analysis.- Palmprint Recognition Combining LBP and Cellular Automata.- Dual Unsupervised Discriminant Projection for Face Recognition.- Applying Wikipedia-Based Explicit Semantic Analysis for Query-Biased Document Summarization.- Special Session on New Hand-Based Biometric Methods.- A New Approach for Vein Pattern-Based Recognition.- Study of Hand-Dorsa Vein Recognition.- DHV Image Registration Using Boundary Optimization.- Special Session on Recent Advances in Image Segmentation.- A Novel Level Set Model Based on Local Information.- A Multi-Descriptor, Multi-Nearest Neighbor Approach for Image Classification.- Orthogonal Locally Discriminant Projection for Palmprint Recognition.- Special Session on Theories and Applications in Advanced Intelligent Computing.- OPC UA Based Information Modeling for Distributed Industrial Systems.- Voting-Averaged Combination Method for Regressor Ensemble.- Face Recognition Using the Feature Fusion Technique Based on LNMF and NNSC Algorithms.- A PDOC Method for Topology Optimization Design.- Special Session on Search Based Software Engineering.- A Decision Support System Based on GIS for Grain Logistics Vehicle Routing Problem.- On Database Normalization Using User Interface Normal Form.- Special Session on Bio-inspired Computing and Applications.- Improved Particle Swarm Optimizers with Application on Constrained Portfolio Selection.- An Improved Image Rectification Algorithm Based on Particle Swarm Optimization.- Particle Swarm Optimizer Based on Small-World Topology and Comprehensive Learning.- Multi-Objective PSO Based on Evolutionary Programming.- Special Session on Advance in Dimensionality Reduction Methods and Its Applications.- Two-Dimensional Sparse Principal Component Analysis for Palmprint Recognition.- Discovery of Protein's Multifunction and Diversity of Information Transmission.- Special Session on Protein and Gene Bioinformatics: Methods and Applications.- Identification and Analysis of Binding Site Residues in Protein Complexes: Energy Based Approach.- Density Based Merging Search of Functional Modules in Protein-Protein Interaction (PPI) Networks.- Topology Prediction of ?-Helical and ?-Barrel Transmembrane Proteins Using RBF Networks.- Palmprint Recognition Based on Neighborhood Rough Set.- Increasing Reliability of Protein Interactome by Combining Heterogeneous Data Sources with Weighted Network Topological Metrics.- Predicting Protein Stability Change upon Double Mutation from Partial Sequence Information Using Data Mining Approach.- Inference of Gene Expression Regulation via microRNA Transfection.- A Residual Level Potential of Mean Force Based Approach to Predict Protein-Protein Interaction Affinity.
• (source: Nielsen Book Data)

The International Conference on Intelligent Computing (ICIC) was formed to provide an annual forum dedicated to the emerging and challenging topics in artificial intel- gence, machine learning, pattern recognition, image processing, bioinformatics, and computational biology. It aims to bring together researchers and practitioners from both academia and industry to share ideas, problems, and solutions related to the m- tifaceted aspects of intelligent computing. ICIC 2010, held in Changsha, China, August 18-21, 2010, constituted the 6th - ternational Conference on Intelligent Computing. It built upon the success of ICIC 2009, ICIC 2008, ICIC 2007, ICIC 2006, and ICIC 2005 that were held in Ulsan, Korea, Shanghai, Qingdao, Kunming and Hefei, China, respectively. This year, the conference concentrated mainly on the theories and methodologies as well as the emerging applications of intelligent computing. Its aim was to unify the picture of contemporary intelligent computing techniques as an integral concept that highlights the trends in advanced computational intelligence and bridges theoretical research with applications. Therefore, the theme for this conference was "Advanced Intelligent Computing Technology and Applications". Papers focusing on this theme were solicited, addressing theories, methodologies, and applications in science and technology.
(source: Nielsen Book Data)

• Cyber System.- Cyber-Physical Systems: A New Frontier.- Security.- Misleading Learners: Co-opting Your Spam Filter.- Survey of Machine Learning Methods for Database Security.- Identifying Threats Using Graph-based Anomaly Detection.- On the Performance of Online Learning Methods for Detecting Malicious Executables.- Efficient Mining and Detection of Sequential Intrusion Patterns for Network Intrusion Detection Systems.- A Non-Intrusive Approach to Enhance Legacy Embedded Control Systems with Cyber Protection Features.- Image Encryption and Chaotic Cellular Neural Network.- Privacy.- From Data Privacy to Location Privacy.- Privacy Preserving Nearest Neighbor Search.- Reliability.- High-Confidence Compositional Reliability Assessment of SOA-Based Systems Using Machine Learning Techniques.- Model, Properties, and Applications of Context-Aware Web Services.
• (source: Nielsen Book Data)

Many networked computer systems are far too vulnerable to cyber attacks that can inhibit their functioning, corrupt important data, or expose private information. Not surprisingly, the field of cyber-based systems is a fertile ground where many tasks can be formulated as learning problems and approached in terms of machine learning algorithms. This book contains original materials by leading researchers in the area and covers applications of different machine learning methods in the reliability, security, performance, and privacy issues of cyber space. It enables readers to discover what types of learning methods are at their disposal, summarizing the state-of-the-practice in this significant area, and giving a classification of existing work.Those working in the field of cyber-based systems, including industrial managers, researchers, engineers, and graduate and senior undergraduate students will find this an indispensable guide in creating systems resistant to and tolerant of cyber attacks.
(source: Nielsen Book Data)

• Title page; Preface; Conference Organization; Contents; Invited Talks; Neural Networks; Computer Vision; Applications; Machine Learning; Reasoning; Planning and Robotics; Multiagent Systems; Author Index.

Provides a representative selection of papers promoting synergies in the research community and includes papers on: Neural Networks, Computer Vision, Applications, Machine Learning, Reasoning, Planning and Robotics, and Multi-Agent Systems. These papers are useful to those computer scientists' or engineers' interested in Artificial Intelligence.

• Preface. Acknowledgments.
• Chapter 1. Introduction. 1.1 The Machine Intelligence Research. 1.2 The Two-Fold Objectives: Data-Driven and Biologically-Inspired Approaches. 1.3 How to Read this Book. 1.4 Summary and Further Reading. References.
• Chapter 2. Incremental Learning. 2.1 Introduction. 2.2 Problem Foundation. 2.3 An Adaptive Incremental Learning Framework. 2.4 Design of the Mapping Function. 2.5 Case Study. 2.6 Summary.
• Chapter 3. Imbalanced Learning. 3.1 Introduction. 3.2 Nature of the Imbalanced Learning. 3.3 Solutions for Imbalanced Learning. 3.4 Assessment Metrics for Imbalanced Learning. 3.5 Opportunities and Challenges. 3.6 Case Study. 3.7 Summary.
• Chapter 4. Ensemble Learning. 4.1 Introduction. 4.2 Hypothesis Diversity. 4.3 Developing Multiple Hypotheses. 4.4 Integrating Multiple Hypotheses. 4.5 Case Study. 4.6 Summary.
• Chapter 5. Adaptive Dynamic Programming for Machine Intelligence. 5.1 Introduction. 5.2 Fundamental Objectives: Optimization and Prediction. 5.3 ADP for Machine Intelligence. 5.4 Case Study. 5.5 Summary.
• Chapter 6. Associative Learning. 6.1 Introduction. 6.2 Associative Learning Mechanism. 6.3 Associative Learning in Hierarchical Neural Networks. 6.4 Case Study. 6.5 Summary.
• Chapter 7. Sequence Learning. 7.1 Introduction. 7.2 Foundations for Sequence Learning. 7.3 Sequence Learning in Hierarchical Neural Structure. 7.4 Level 0: A Modified Hebbian Learning Architecture. 7.5 Level 1 to Level N: Sequence Storage, Prediction and Retrieval. 7.6 Memory Requirement. 7.7 Learning and Anticipation of Multiple Sequences. 7.8 Case Study. 7.9 Summary.
• Chapter 8. Hardware Design for Machine Intelligence. 8.1 A Final Comment. References.
• (source: Nielsen Book Data)

This book will advance the understanding and application of self-adaptive intelligent systems; therefore it will potentially benefit the long-term goal of replicating certain levels of brain-like intelligence in complex and networked engineering systems. It will provide new approaches for adaptive systems within uncertain environments. This willprovide an opportunity to evaluate the strengths and weaknesses of the current state-of-the-art of knowledge, give rise to new research directions, and educate future professionals in this domain. Self-adaptive intelligent systems have wide applications from military security systems to civilian daily life. In this book, different application problems, including pattern recognition, classification, image recovery, and sequence learning, will be presented to show the capability of the proposed systems in learning, memory, and prediction. Therefore, this book will also provide potential new solutions to many real-world applications.
(source: Nielsen Book Data)

• Local Ordinal Classification.- Using Genetic Algorithms and Decision Trees for a posteriori Analysis and Evaluation of Tutoring Practices based on Student Failure Models.- Exploiting Decision Trees in Product-based Fuzzy Neural Modeling to Generate Rules with Dynamically Reduced Dimensionality.- Retraining the Neural Network for Data Visualization.- Rule-Based Adaptive Navigation for an Intelligent Educational Mobile Robot.- BRWM: A relevance feedback mechanism for web page clustering.- Bagged Averaging of Regression Models.- Argument-based User Support Systems using Defeasible Logic Programming.- Knowledge Modelling Using The UML Profile.- Optimized Multi-Domain Secure Interoperation using Soft Constraints.- Learning context models for the recognition of scenarios.- MDA-Based Architecture of a Description Logics Reasoner.- Incremental guideline formalization with tool support.- Accessing data in the semantic web: An intelligent data integration and navigation approaches.- An Expert System Delivery Environment for the World Wide Web.- A Formally Specified Ontology Management API as a Registry for Ubiquitous Computing Systems.- Intelligent Configurable Electronic Shop Platform based on Ontologies and 3D Visualization.- Adapting User Interfaces to the User Interaction Requirements in Real Time.- On supporting young students in visual logic modeling.- Attentional Model for Perceiving Social Context in Intelligent Environments.- Penguin Quart - Slovak Digit Speech Recognition Game Based on HMM.- Impact of Face Registration Errors on Recognition.- Unsupervised Segmentation of Meeting Configurations and Activities using Speech Activity Detection.- A Model of Real-Time Indoor Surveillance System using Behavior Detection.- A Filter Module Used in Pedestrian Detection System.- User Localization for Intelligent Crisis Management.- An Intrusion Detection System for Network-Initiated Attacks Using a Hybrid Neural Network.- Which Adequate Trust Model for Trust Networks?.- XML Systems for Intelligent Management of Pervasive Computing Resources.- A constraint based approach for aiding heat treatment operation design and distortion evaluation.- Applying AI to Cooperating Agricultural Robots.- Capacity Evaluation of an Indoor Smart Antenna System at 60 GHz.- Steady State Contingency analysis of electrical networks using machine learning techniques.- Robust Multimodal Audio-Visual Processing for Advanced Context Awareness in Smart Spaces.- Toward supporting group dynamics.- Multimodal Integration of Sensor Network.- Multimodal Identity Tracking in a Smartroom.- Multimodal Focus Attention and Stress Detection and feedback in an Augmented Driver Simulator.- A Fuzzy Expert System for the Early Warning of Accidents Due to Driver Hypo-Vigilance.- Mixed Reality Cane Simulation.- 3D content-based search using sketches.- Manual Annotation and Automatic Image Processing of Multimodal Emotional Behaviors in TV Interviews.- MPEG-4 Facial Expression Synthesis based on Appraisal Theory.- Towards On- and Off-line Search, Browse and Replay of Home Activities.- Engineering an interoperable adaptive hypermedia testing tool supporting user adaptable strategies.- Teaching a Computer Science Course using Adaptable Instructional Images.- e-Class Personalized: Design and Evaluation of an Adaptive Learning Content Management System.- The Use of Psychophysiological Measures for Designing Adaptive Learning Systems.- Developing Personalized E-Books: A Multi-Layered Approach.- Designing a Solver for Arithmetic Constraints to Support Education in Mathematics.- A Tutoring System Discovering Gaps in the Current Body of Students' Knowledge.- Sequencing Parametric Exercises for an Operating System Course.- A gene expression analysis system for medical diagnosis.- Recording, Monitoring and Interrelating Changes of Invivo Bio-cells from Video (Biosignatures).- An Archetype for MRI guided Tele-interventions.- Differential Evolution Algorithms for Finding Predictive Gene Subsets in Microarray Data.- Feature Selection for Microarray Data Analysis Using Mutual Information and Rough Set Theory.- A Support Vector Machine Approach to Breast Cancer Diagnosis and Prognosis.- Source Code Author Identification Based on N-gram Author Profiles.- AJA - Tool for Programming Adaptable Agents.- Investigating the Predictability of Empirical Software Failure Data with Artificial Neural Networks and Hybrid Models.- Selecting the Appropriate Machine Learning Techniques for the Prediction of Software Development Costs.- On the Idea of Using Nature-Inspired Metaphors to Improve Software Testing.- Fast Video Object Tracking using Affine Invariant Normalization.- Knowledge Acquisition from Multimedia Content using an Evolution Framework.- Exploratory Search: Image Retrieval without Deep Semantics.- Word Senses: The Stepping Stones in Semantic-Based Natural Language Processing.- Space-Time Tubes and Motion Representation.- Semantic Concept Detection from News Videos with Self-Organizing Maps.- Analysis of Semantic Information Available in an Image Collection Augmented with Auxiliary Data.- Supporting Semi-Automatic Semantic Annotation of Multimedia Resources.- A Simulation Tool for Modelling Pedestrian Dynamics during Evacuation of Large Areas.- Radar Imaging by Range Density Functions.- A Method for Incremental Data Fusion in Distributed Sensor Networks.- Information Society: the two faces of Janus.- Ant Seeker: An algorithm for enhanced web search.- Increasing Intelligent Wireless Sensor Networks Survivability by Applying Energy-Efficient Schemes.- A Review of Video Watermarking and a Benchmarking Framework.- Chaotic Encryption Driven Watermarking of Human Video Objects Based on Hu Moments.- Semi-Fragile Watermarking Authentication with Local and Global Watermarks.- Decentralising the Digital Rights Management Value Chain by means of Distributed License Catalogues.- AXMEDIS architectural solution for interoperable content and DRM on multichannel distribution.- Computer Aided Diagnosis of CT Focal Liver Lesions based on Texture Features, Feature Selection and Ensembles of Classifiers.- Texture Analysis for Classification of Endometrial Tissue in Gray Scale Transvaginal Ultrasonography.- Wavelet-based Feature Analysis for Classification of Breast Masses from Normal Dense Tissue.- Microcalcification Features Extracted from Principal Component Analysis in the Wavelet Domain.- Classification of Atherosclerotic Carotid Plaques Using Gray Level Morphological Analysis on Ultrasound images.
• (source: Nielsen Book Data)

Artificial Intelligence applications build on a rich and proven theoretical background to provide solutions to a wide range of real life problems. The ever expanding abundance of information and computing power enables researchers and users to tackle higly interesting issues for the first time, such as applications providing personalized access and interactivity to multimodal information based on preferences and semantic concepts or human-machine interface systems utilizing information on the affective state of the user. The purpose of the 3rd IFIP Conference on Artificial Intelligence Applications and Innovations (AIAI) is to bring together researchers, engineers, and practitioners interested in the technical advances and business and industrial applications of intelligent systems. AIAI 2006 is focused on providing insights on how AI can be implemented in real world applications.
(source: Nielsen Book Data)

• Title page; Preface; Conference Organization; Contents; Full Papers; Cognitive Learning with Automatic Goal Acquisition; Semantics of Alan; A Compact Argumentation System for Agent System Specification; Social Responsibility Among Deliberative Agents; Knowledge Base Extraction for Fuzzy Diagnosis of Mental Retardation Level; Tuning the Feature Space for Content-Based Music Retrieval; Personalizing Trust in Online Auctions; An Hybrid Soft Computing Approach for Automated Computer Design; FUNEUS: A Neurofuzzy Approach Based on Fuzzy Adaline Neurons; Empirical Evaluation of Scoring Methods

• Binarization Algorithms for Approximate Updating in Credal NetsOn Generalizing the AGM Postulates; The Two-Variable Situation Calculus; Base Belief Change and Optimized Recovery; Unsupervised Word Sense Disambiguation Using the WWW; Relational Descriptive Analysis of Gene Expression Data; Solving Fuzzy PERT Using Gradual Real Numbers; Approaches to Efficient Resource-Constrained Project Rescheduling; A Comparison of Web Service Interface Similarity Measures; Finding Alternatives Web Services to Parry Breakdowns; Posters; Smart Ride Seeker Introductory Plan

• Spam Filtering: The Influence of the Temporal Distribution of Training DataAn Approach for Evaluating User Model Data in an Interoperability Scenario; On the Improvement of Brain Tumour Data Clustering Using Class Information; Endowing BDI Agents with Capability for Modularizing; Rational Agents Under ASP in Games Theory; Automatic Generation of Natural Language Parsers from Declarative Specifications; Reconsideration on Non-Linear Base Orderings; Dynamic Abstraction for Hierarchical Problem Solving and Execution in Stochastic Dynamic Environments

• A Comparison of Two Machine-Learning Techniques to Focus the Diagnosis TaskArgumentation Semantics for Temporal Defeasible Logic; NEWPAR: An Optimized Feature Selection and Weighting Schema for Category Ranking; Challenges and Solutions for Hierarchical Task Network Planning in E-Learning; Invited Talks; Artificial Intelligence and Unmanned Aerial Vehicles; Writing a Good Grant Proposal; Author Index

STAIRS 2006 is the third European Starting AI Researcher Symposium, an international meeting aimed at AI researchers, from all countries, at the beginning of their career: PhD students or people holding a PhD for less than one year. The topics of the papers included range from traditional AI areas to AI applications, such as Agents, Automated Reasoning, Belief Revision, Case-based Reasoning, Constraints, Data Mining & Information Extraction, Genetic Algorithms, Human Computer Interaction, Interactive Sensry Systems (Speech, Multi-Model Processing), Knowledge Representation, Logic Programming, Machine Learning, Natural Language Processing, Neural Networks, Nonmonotonic Reasoning, Planning & Scheduling, Reasoning about Action and Change, Robotics, Search, Smantic Web, Spatial & Temporal Reasoning and Uncertainty.

The book includes all the background material required to understand the principles underlying intelligence, as well as enough detailed information on intelligent robotics and simulated agents so readers can begin experiments and projects on their own. By the mid-1980s researchers from artificial intelligence, computer science, brain and cognitive science, and psychology realized that the idea of computers as intelligent machines was inappropriate. The brain does not run "programs"; it does something entirely different. But what? Evolutionary theory says that the brain has evolved not to do mathematical proofs but to control our behavior, to ensure our survival. Researchers now agree that intelligence always manifests itself in behavior-thus it is behavior that we must understand. An exciting new field has grown around the study of behavior-based intelligence, also known as embodied cognitive science, "new AI, " and "behavior-based AI." This book provides a systematic introduction to this new way of thinking. After discussing concepts and approaches such as subsumption architecture, Braitenberg vehicles, evolutionary robotics, artificial life, self-organization, and learning, the authors derive a set of principles and a coherent framework for the study of naturally and artificially intelligent systems, or autonomous agents. This framework is based on a synthetic methodology whose goal is understanding by designing and building. The book includes all the background material required to understand the principles underlying intelligence, as well as enough detailed information on intelligent robotics and simulated agents so readers can begin experiments and projects on their own. The reader is guided through a series of case studies that illustrate the design principles of embodied cognitive science.
(source: Nielsen Book Data)

• A Prescriptive Analytical Logic Model Incorporated with Analytic Hierarchy Process for Software Application Error Analysis
• Simplified Framework of Natural Language Processing for Structure Management of Current-Age Data
• Design and Software Implementation of Heuristic and Suboptimal Strategies for the Mancala/Kalah Game
• Sector-Selective Hybrid Scheme Facilitating Hardware Supportability Over Image Compression
• Performance Evaluation of Joint Rate-Distortion Model of Video Codec
• To Defeat DDoS Attacks in Cloud Computing Environment Using Software Defined Networking (SDN)
• Oriented Petri Nets as Means of Describing a Human-Computer Interaction
• FPGA Based Transient Fault Generate and Fault Tolerant for Asynchronous and Synchronous Circuits
• Designing an Energy Efficient Routing for Subsystems Sensors in Internet of Things Eco-System Using Distributed Approach
• The Compare of Solo Programming and Pair Programming Strategies in a Scrum Team: A Multi-agent Simulation
• Fair Random Access with Track-Based SNR Scheduling for Full-Duplex Wireless Powered Communication Networks
• A DTN Gateway-Based Architecture for Web Access in Space Internet
• Multi-objective Scheduling Optimization for ETL Tasks in Cluster Environment
• An LSTM-Based Encoder-Decoder Model for State-of-Charge Estimation of Lithium-Ion Batteries
• Analysis of Social Engineering Attacks Using Exploit Kits
• Bipolar Disorder: A Pathway Towards Research Progress in Identification and Classification
• Managing Electronic Waste with Recycling: A Review of Developing and Developed Regions
• Trajectory Modeling in a Pursuit Problem with Curvature Restrictions
• An Approach to Using Templates for Modeling Exceptions in Terms of Petri Nets
• Performance Bottleneck Analysis and Optimization Framework in Concurrent Environments
• Matlab Code Generation and Consumption
• The Problem of Preventing the Development of Critical Combinations of Events in Large-Scale Systems
• Using Software Package "Multimeat-Expert" for Modeling and Optimization of Composition Chopped Meat Product with Vegetable Additive
• Application of Machine Learning for Document Classification and Processing in Adaptive Information Systems
• SNR-Based TDMA Scheduling with Continuous Energy Transfer for Wireless Powered Communication Networks
• Comparative Analysis of Software Development Life Cycle Models (SDLC). Plus 27 more papers.

This book gathers the refereed proceedings of the Intelligent Algorithms in Software Engineering Section of the 9th Computer Science On-line Conference 2020 (CSOC 2020), held on-line in April 2020. Software engineering research and its applications to intelligent algorithms have now assumed an essential role in computer science research. In this book, modern research methods, together with applications of machine and statistical learning in software engineering research, are presented. .
(source: Nielsen Book Data)

• Chapter 1. Contraction methods for correlation clustering: the order is important (Laszlo Aszalos).-
• Chapter 2. Optimisation of Preparedness and Response of Health services in major crises using the IMPRESS platform (Nina Dobrinkova).-
• Chapter 3. The new approach for dynamic optimization with variability constraints (Pawel Drag).-
• Chapter 4. Intercriteria Analisys of ACO Performance for Workforce Planning Problem (Olympia Roeva).-
• Chapter 5. Is Prufer Code encoding always a bad idea? (Hanno Hildmann).-
• Chapter 6. A model for wireless-access network topology and a PSO-based approach for its optimization (Hanno Hildmann).-
• Chapter 7. InterCriteria Analysis Approach for Comparison of Simple and Multi-population Genetic Algorithms Performance (Maria Angelova).-
• Chapter 8. Structure Optimization and Learning of Fuzzy Cognitive Map with the use of Evolutionary Algorithm and Graph Theory Metrics (Katarzyna Poczeta).-
• Chapter 9. Fuzziness in the Berth Allocation Problem (Flabio Gutierrez).-
• Chapter 10. Identifying Clusters in Spatial Data via Sequential Importance Sampling (Nishanthi Raveendran).-
• Chapter 11. Multiobjective Optimization Grover Adaptive Search (Benjamin Baran.-
• Chapter 12. Discovering Knowledge from Predominantly Repetitive Data by InterCriteria Analysis (Olympia Roeva).
• (source: Nielsen Book Data)

Our everyday lives are practically unthinkable without optimization. We constantly try to minimize our effort and to maximize the reward or progress achieved. Many real-world and industrial problems arising in engineering, economics, medicine and other domains can be formulated as optimization tasks. This volume presents a comprehensive collection of extended contributions from the 2017 Workshop on Computational Optimization. Presenting recent advances in computational optimization, it addresses important concrete applications, e.g. the modeling of physical processes, wildfire modeling, modeling processes in chemical engineering, workforce planning, wireless access network topology, parameter settings for controlling various processes, berth allocation, identification of homogeneous domains, and quantum computing. The book shows how to develop algorithms for them based on new intelligent methods like evolutionary computations, ant colony optimization, constrain programming and others.
(source: Nielsen Book Data)

• Table of Contents Recognizing traffic signs using Convnets Annotating Images with Object Detection API Caption generation for images Building GANs for Conditional Image Creation Stock Price Prediction with LSTM Create & Train Machine Translation Systems Train and set up a Chatbot, able to discuss like a human Detecting Duplicate Quora Questions Building a TensorFlow Recommender Systems Video Games by Reinforcement learning.
• (source: Nielsen Book Data)

Leverage the power of Tensorflow to design deep learning systems for a variety of real-world scenarios Key Features Build efficient deep learning pipelines using the popular Tensorflow framework Train neural networks such as ConvNets, generative models, and LSTMs Includes projects related to Computer Vision, stock prediction, chatbots and more Book DescriptionTensorFlow is one of the most popular frameworks used for machine learning and, more recently, deep learning. It provides a fast and efficient framework for training different kinds of deep learning models, with very high accuracy. This book is your guide to master deep learning with TensorFlow with the help of 10 real-world projects. TensorFlow Deep Learning Projects starts with setting up the right TensorFlow environment for deep learning. Learn to train different types of deep learning models using TensorFlow, including Convolutional Neural Networks, Recurrent Neural Networks, LSTMs, and Generative Adversarial Networks. While doing so, you will build end-to-end deep learning solutions to tackle different real-world problems in image processing, recommendation systems, stock prediction, and building chatbots, to name a few. You will also develop systems that perform machine translation, and use reinforcement learning techniques to play games. By the end of this book, you will have mastered all the concepts of deep learning and their implementation with TensorFlow, and will be able to build and train your own deep learning models with TensorFlow confidently. What you will learn Set up the TensorFlow environment for deep learning Construct your own ConvNets for effective image processing Use LSTMs for image caption generation Forecast stock prediction accurately with an LSTM architecture Learn what semantic matching is by detecting duplicate Quora questions Set up an AWS instance with TensorFlow to train GANs Train and set up a chatbot to understand and interpret human input Build an AI capable of playing a video game by itself -and win it! Who this book is forThis book is for data scientists, machine learning developers as well as deep learning practitioners, who want to build interesting deep learning projects that leverage the power of Tensorflow. Some understanding of machine learning and deep learning, and familiarity with the TensorFlow framework is all you need to get started with this book.
(source: Nielsen Book Data)

• Part I Computational Models and Knowledge Discovery
• Part II Communications, Networks, and Cloud Computing
• Part III Computer-Based Systems
• Part IV Data-Oriented and Software-Intensive Systems.

This carefully edited and reviewed volume addresses the increasingly popular demand for seeking more clarity in the data that we are immersed in. It offers excellent examples of the intelligent ubiquitous computation, as well as recent advances in systems engineering and informatics. The content represents state-of-the-art foundations for researchers in the domain of modern computation, computer science, system engineering and networking, with many examples that are set in industrial application context. The book includes the carefully selected best contributions to APCASE 2014, the 2nd Asia-Pacific Conference on Computer Aided System Engineering, held February 10-12, 2014 in South Kuta, Bali, Indonesia. The book consists of four main parts that cover data-oriented engineering science research in a wide range of applications: computational models and knowledge discovery; communications networks and cloud computing; computer-based systems; and data-oriented and software-intensive systems.

• Deep Learning of Representations / Yoshua Bengio, Aaron Courville
• Recurrent Neural Networks / Sajid A. Marhon, Christopher J.F. Cameron
• Supervised Neural Network Models for Processing Graphs / Monica Bianchini, Marco Maggini
• Topics on Cellular Neural Networks / Liviu Goraş, Ion Vornicu, Paul Ungureanu
• Approximating Multivariable Functions by Feedforward Neural Nets / Paul C. Kainen, Věra Kůrková
• Bochner Integrals and Neural Networks / Paul C. Kainen, Andrew Vogt
• Semi-supervised Learning / Mohamed Farouk Abdel Hady, Friedhelm Schwenker
• Statistical Relational Learning / Hendrik Blockeel
• Kernel Methods for Structured Data / Andrea Passerini
• Multiple Classifier Systems: Theory, Applications and Tools / Francesco Gargiulo, Claudio Mazzariello
• Self Organisation and Modal Learning: Algorithms and Applications / Dominic Palmer-Brown, Chrisina Jayne
• Bayesian Networks, Introduction and Practical Applications / Wim Wiegerinck, Willem Burgers, Bert Kappen
• Relevance Feedback in Content-Based Image Retrieval: A Survey / Jing Li, Nigel M. Allinson
• Learning Structural Representations of Text Documents in Large Document Collections / Ah Chung Tsoi, Markus Hagenbuchner, Milly Kc
• Neural Networks in Bioinformatics / Masood Zamani, Stefan C. Kremer.

This handbook presents some of the most recent topics in neural information processing, covering both theoretical concepts and practical applications. The contributions include: Deep architectures Recurrent, recursive, and graph neural networks Cellular neural networks Bayesian networks Approximation capabilities of neural networks Semi-supervised learning Statistical relational learning Kernel methods for structured data Multiple classifier systems Self organisation and modal learning Applications to content-based image retrieval, text mining in large document collections, and bioinformat.

• Stochastic Algorithms for 3D Node Localization in Anisotropic Wireless Sensor Networks
• An Evaluation of Classification Algorithms Using Mc Nemar's Test
• Permitting Features in P Systems Generating Picture Arrays
• An ACO Framework for Single Track Railway Scheduling Problem
• Bio-Inspired Soft-Computational Framework for Speech and Image Application
• Leukocyte Classification in Skin Tissue Images
• Solving Application Oriented Graph Theoretical Problems with DNA Computing
• Human Identification Using Heartbeat Interval Features and ECG Morphology
• Improved Real-Time Discretize Network Intrusion Detection System
• Identification and Impact Assessment of High-Priority Field Failures in Passenger Vehicles Using Evolutionary Optimization
• Automatic Agricultural Leaves Recognition System
• Non-Uniform Mapping in Binary-Coded Genetic Algorithms.

• Control Words of Transition P Systems
• Iso-Array Splicing Grammar System
• GA based Dimension Reduction for enhancing performance of k-Means and Fuzzy k-Means: A Case Study for Categorization of Medical Dataset
• A Computational Intelligence based Approach to Telecom Customer Classification for Value Added Services
• An Efficient Approach on Rare Association Rule Mining
• A Hybrid Multiobjective Particle Swarm Optimization Approach for Non-redundant Gene Marker Selection
• Application of High Quality Amino Acid Indices to AMS 3.0: A Update Note
• Constructive Solid Geometry Based Topology Optimization Using Evolutionary Algorithm
• Array P Systems with Hybrid Teams
• An Approach for the Ordering of Evaluation of Objectives in Multiobjective Optimization
• Extended Forma: Analysis and an Operator Exploiting it
• Incorporating Great Deluge with Harmony Search for Global Optimization Problems.

• Boundary Handling Approaches in Particle Swarm Optimization
• Diversity Measures in Artificial Bee Colony
• Digital Video Watermarking Using Scene Detection
• Self Adaptive Acceleration Factor in Particle Swarm Optimization
• Applying Case Based Reasoning in Cuckoo Search for the Expedition of Groundwater Exploration
• Reversible OR Logic Gate Design Using DNA
• Performance Enhanced Hybrid Artificial Neural Network for Abnormal Retinal Image Classification
• Algorithmic Tile Self-assembly Model for the Minimum Dominating Set Problem
• Semantic Sub-tree Crossover Operator for Postfix Genetic Programming
• Exploration Enhanced Particle Swarm Optimization using Guided Re-Initialization
• Using Firefly Algorithm to Solve Resource Constrained Project Scheduling Problem
• Analysis of Cellular Automata and Genetic Algorithm based Test Pattern Generators for Built In Self Test
• Ant Colony-based System for Retinal Blood Vessels Segmentation.

• An Efficient Neural Network Based Background Subtraction Method
• JustThink: Smart BCI Applications
• Interpretability Issues in Evolutionary Multi-Objective Fuzzy Knowledge Base Systems
• Hybrid Firefly Based Simultaneous Gene Selection and Cancer Classification Using Support Vector Machines and Random Forests
• Recognition of Online Handwritten Gurmukhi Strokes Using Support Vector Machine
• An Optimal Fuzzy Logic Controller Tuned with Artificial Immune System
• Comparisons of Different Feature Sets for Predicting Carbohydrate-Binding Proteins From Amino Acid Sequences Using Support Vector Machine
• A PSO Based Smart Unit Commitment Strategy for Power Systems Including Solar Energy
• A User-Oriented Content Based Recommender System Based on Reclusive Methods and Interactive Genetic Algorithm.

The book is a collection of high quality peer reviewed research papers presented in Seventh International Conference on Bio-Inspired Computing (BIC-TA 2012) held at ABV-IIITM Gwalior, India. These research papers provide the latest developments in the broad area of "Computational Intelligence". The book discusses wide variety of industrial, engineering and scientific applications of nature/bio-inspired computing and presents invited papers from the inventors/originators of novel computational techniques